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from typing import Tuple |
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import gradio |
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import torch |
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import transformers |
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from HGTrainer import RobertaForValNovRegression, ValNovOutput |
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@torch.no_grad() |
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def classify(model, premise, conclusion): |
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model_path = "pheinisch/" |
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if model == "model trained only with task-internal data": |
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model_path += "ConclusionValidityNoveltyClassifier-Augmentation-in_750" |
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elif model == "model trained with 1k task-external+synthetic data": |
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model_path += "ConclusionValidityNoveltyClassifier-Augmentation-ext_syn_1k" |
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elif model == "model trained with 10k task-internal-external-synthetic data": |
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model_path += "ConclusionValidityNoveltyClassifier-Augmentation-int_ext_syn_10k" |
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elif model == "model trained with 750 task-internal-synthetic data": |
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model_path += "ConclusionValidityNoveltyClassifier-Augmentation-in_syn_750" |
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hg_model: RobertaForValNovRegression = RobertaForValNovRegression.from_pretrained(model_path) |
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hg_model.loss = "ignore" |
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hg_tokenizer: transformers.RobertaTokenizer = \ |
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transformers.AutoTokenizer.from_pretrained("{}-base".format(hg_model.config.model_type)) |
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output: ValNovOutput = hg_model(**hg_tokenizer(text=[premise], |
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text_pair=[conclusion], |
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max_length=156, |
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padding=True, |
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truncation=True, |
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is_split_into_words=False, |
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return_tensors="pt")) |
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validity = output.validity.cpu().item() |
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novelty = output.novelty.cpu().item() |
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return { |
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"valid and novel": validity*novelty, |
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"valid but not novel": validity*(1-novelty), |
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"not valid but novel": (1-validity)*novelty, |
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"rubbish": (1-validity)*(1-novelty) |
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} |
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gradio.Interface( |
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fn=classify, |
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inputs=[ |
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gradio.Dropdown(choices=["model trained only with task-internal data", |
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"model trained with 1k task-external+synthetic data", |
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"model trained with 10k task-internal-external-synthetic data", |
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"model trained with 750 task-internal-synthetic data"], |
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value="model trained with 750 task-internal-synthetic data", |
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label="Classifier"), |
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gradio.Textbox(value="Whatever begins to exist has a cause of its existence. The universe began to exist.", |
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max_lines=5, |
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label="Premise"), |
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gradio.Textbox(value="Therefore, the universe has a cause of its existence (God).", |
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max_lines=2, |
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label="Conclusion") |
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], |
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outputs=gradio.Label(value="Try out your premise and conclusion ;)", |
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num_top_classes=2, |
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label="Validity-Novelty-Output"), |
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examples=[ |
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["model trained with 1k task-external+synthetic data", |
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"Whatever begins to exist has a cause of its existence. The universe began to exist.", |
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"Therefore, the universe has a cause of its existence (God)."], |
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["model trained with 750 task-internal-synthetic data", |
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"Humans have the capability to think about more than food or mecanical stuff, they have the capability to pay " |
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"attention to morality, having a conscience, ask for philosophical question, thinking about the sense of " |
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"life, where I'm coming from, where I'll go...", |
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"We should order a pizza"], |
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["model trained with 750 task-internal-synthetic data", |
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"I plan to walk, but the weather forecast prognoses rain.", |
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"I should take my umbrella"] |
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], |
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title="Predicting validity and novelty", |
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description="Demo for the paper: \"Data Augmentation for Improving the Prediction of Validity and Novelty of " |
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"Argumentative Conclusions\". Consider the repo: https://github.com/phhei/ValidityNoveltyRegressor" |
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).launch() |
